Learning to Identify the Best Contexts for Knowledge-based WSD

Abstract

We outline a learning framework that aims at identifying useful contextual cues for knowledge-based word sense disambiguation. The usefulness of individual context words is evaluated based on diverse lexico-statistical and syntactic information, as well as simple word distance. Experiments using two different knowledge-based methods and benchmark datasets show significant improvements due to context modeling, beating the conventional window-based approach.